Feb 1, 2018 · We describe an end-to-end trainable model for image compression based on variational autoencoders. The model incorporates a hyperprior to effectively capture spatial …

We present an end-to-end trainable image compression framework for low bit-rate image compression. Our method is based on variational autoencoder, which consists of a nonlinear …

May 31, 2023 · This guide will explore using Variational Autoencoders for image compression, its working principles, and how to implement a VAE model using PyTorch. We will also use …

This repository provides a comparative analysis of Variational Autoencoders (VAE) and Autoencoders (AE) for image compression, focusing on regularization techniques and noise …

Nov 1, 2021 · Variational Autoencoder (VAE), as a kind of deep hidden space generation model, has achieved great success in performance in recent years, especially in image generation. …

Dec 2, 2022 · In this study, a medical image data compression method based on a variational autoencoder is studied. First, the basic theoretical knowledge of variational autoencoder and …

In this paper, we propose a noise-to-compression variational antoencoder (NC-VAE) to achieve efficient rate-distortion optimization (RDO) for end-to-end optimized image compression with a …

The idea is simple: instead of storing the original input image (which would take up a lot of space), we store its compressed representation in the latent space. This way, when someone wants to …

Specifically, the model that Stable Diffusion uses is called a Variational AutoEncoder. What is a Variational AutoEncoder? To begin, let's develop a high level intuition about the Variational...

Based on this effective frame-work, we present an end-to-end image compression method with a multi-scale encoder, residual decoder, and separate entropy model. The encoder uses a …